Pt/MoO3 nano-flower/SiC Schottky diode based hydrogen gas sensor

In this paper, we report the development of a novel Pt/MoO3 nano-flower/SiC Schottky diode based device for hydrogen gas sensing applications. The MoO3 nanostructured thin films were deposited on SiC substrates via thermal evaporation. Morphological characterization of the nanostructured MoO3 by scanning electron microscopy revealed randomly orientated thin nanoplatelets in a densely packed formation of nano-flowers with dimensions ranging from 250 nm to 1 μm. Current-voltage characteristics of the sensor were measured at temperatures from 25°C to 250°C. The sensor showed greater sensitivity in a reverse bias condition than in forward bias. Dynamic response of the sensor was investigated towards different concentrations of hydrogen gas in a synthetic air mixture at 250°C and a large voltage shift of 5.7 V was recorded upon exposure to 1% hydrogen.

Pt/anodized TiO2/SiC-based MOS device for hydrocarbon sensing

Pt/anodized TiO2/SiC based metal-oxide-semiconductor (MOS) devices were fabricated and characterized for their sensitivity towards propene (C3H6). Titanium (Ti) thin films were deposited onto the SiC substrates using a filtered cathodic vacuum arc (FCVA) method. Fluoride ions containing neutral electrolyte (0.5 wt% NH4F in ethylene glycol)were used to anodize the Ti films. The anodized films were subsequently annealed at 600 °C for 4 hrs in an oxygen rich environment to obtain TiO2. The current-voltage(I-V) characteristics of the Pt/TiO2/SiC devices were measured in different concentrations of propene. Exposure to the analyte gas caused a change in the Schottky barrier height and hence a lateral shift in the I-V characteristics. The effective change in the barrier height for 1% propene was calculated as 32.8 meV at 620°C. The dynamic response of the sensors was also investigated and a voltage shift of 157 mV was measured at 620°C during exposure to 1% propene.

Image based visual servo control for fixed wing UAVs tracking linear infrastructure in wind

This paper presents an Image Based Visual Servo control design for Fixed Wing Unmanned Aerial Vehicles tracking locally linear infrastructure in the presence of wind using a body fixed imaging sensor. Visual servoing offers improved data collection by posing the tracking task as one of controlling a feature as viewed by the inspection sensor, although is complicated by the introduction of wind as aircraft heading and course angle no longer align. In this work it is shown that the effects of wind alter the desired line angle required for continuous tracking to equal the wind correction angle as would be calculated to set a desired course. A control solution is then sort by linearizing the interaction matrix about the new feature pose such that kinematics of the feature can be augmented with the lateral dynamics of the aircraft, from which a state feedback control design is developed. Simulation results are presented comparing no compensation, integral control and the proposed controller using the wind correction angle, followed by an assessment of response to atmospheric disturbances in the form of turbulence and wind gusts

Passive control of a biventricular assist device with compliant inflow cannulae

Rotary ventricular assist device (VAD) support of the cardiovascular system is susceptible to suction events due to the limited preload sensitivity of these devices. This may be of particular concern with rotary biventricular support (BiVAD) where the native, flow-balancing Starling response is diminished in both ventricles. The reliability of sensor and sensor-less based control systems which aim to control VAD flow based on preload have limitations and thus an alternative solution is desired. This study introduces a compliant inflow cannula (CIC) which could improve the preload sensitivity of a rotary VAD by passively altering VAD flow depending on preload. To evaluate the design, both the CIC and a standard rigid inflow cannula were inserted into a mock circulation loop to enable biventricular heart failure support using configurations of atrial and ventricular inflow, and arterial outflow cannulation. A range of left (LVAD) and right VAD (RVAD) rotational speeds were tested as well as step changes in systemic/pulmonary vascular resistance to alter relative preloads, with resulting flow rates recorded. Simulated suction events were observed, particularly at higher VAD speeds, during support with the rigid inflow cannula, while the CIC prevented suction events under all circumstances. The compliant section passively restricted its internal diameter as preload was reduced, which increased the VAD circuit resistance and thus reduced VAD flow. Therefore, a compliant inflow cannula could potentially be used as a passive control system to prevent suction events in rotary left, right and biventricular support.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke.

Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions

This paper presents large, accurately calibrated and time-synchronised datasets, gathered outdoors in controlled environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. It discusses how the data collection process was designed, the conditions in which these datasets have been gathered, and some possible outcomes of their exploitation, in particular for the evaluation of performance of sensors and perception algorithms for UGVs.

Sensor Data Integrity : Multi-Sensor Perception for Unmanned Ground Vehicles

This document describes large, accurately calibrated and time-synchronised datasets, gathered in controlled environmental conditions, using an unmanned ground vehicle equipped with a wide variety of sensors. These sensors include: multiple laser scanners, a millimetre wave radar scanner, a colour camera and an infra-red camera. Full details of the sensors are given, as well as the calibration parameters needed to locate them with respect to each other and to the platform. This report also specifies the format and content of the data, and the conditions in which the data have been gathered. The data collection was made in two different situations of the vehicle: static and dynamic. The static tests consisted of sensing a fixed ’reference’ terrain, containing simple known objects, from a motionless vehicle. For the dynamic tests, data were acquired from a moving vehicle in various environments, mainly rural, including an open area, a semi-urban zone and a natural area with different types of vegetation. For both categories, data have been gathered in controlled environmental conditions, which included the presence of dust, smoke and rain. Most of the environments involved were static, except for a few specific datasets which involve the presence of a walking pedestrian. Finally, this document presents illustrations of the effects of adverse environmental conditions on sensor data, as a first step towards reliability and integrity in autonomous perceptual systems.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Evaluation of MegaVoltage Cone Beam CT image quality with an unmodified Elekta Precise Linac and EPID : a feasibility study

In order to increase the accuracy of patient positioning for complex radiotherapy treatments various 3D imaging techniques have been developed. MegaVoltage Cone Beam CT (MVCBCT) can utilise existing hardware to implement a 3D imaging modality to aid patient positioning. MVCBCT has been investigated using an unmodified Elekta Precise linac and 15 iView amorphous silicon electronic portal imaging device (EPID). Two methods of delivery and acquisition have been investigated for imaging an anthropomorphic head phantom and quality assurance phantom. Phantom projections were successfully acquired and CT datasets reconstructed using both acquisition methods. Bone, tissue and air were 20 clearly resolvable in both phantoms even with low dose (22 MU) scans. The feasibility of MegaVoltage Cone beam CT was investigated using a standard linac, amorphous silicon EPID and a combination of a free open source reconstruction toolkit as well as custom in-house software written in Matlab. The resultant image quality has 25 been assessed and presented. Although bone, tissue and air were resolvable 2 in all scans, artifacts are present and scan doses are increased when compared with standard portal imaging. The feasibility of MVCBCT with unmodified Elekta Precise linac and EPID has been considered as well as the identification of possible areas for future development in artifact correction techniques to 30 further improve image quality.

A comparison study on hydrogen sensing performance of MoO3 nanoplatelets coated with a thin layer of Ta2O5 or La2O3

There has been significant interest in developing metal oxide films with high surface area-to-volume ratio nanostructures particularly in substantially increasing the performance of Pt/oxide/semiconductor Schottky-diode gas sensors. While retaining the surface morphology of these devices, they can be further improved by modifying their nanostructured surface with a thin metal oxide layer. In this work, we analyse and compare the electrical and hydrogen-sensing properties of MoO3 nanoplatelets coated with a 4 nm layer of tantalum oxide (Ta2O5) or lanthanum oxide (La2O3). We explain in our study, that the presence of numerous defect traps at the surface (and the bulk) of the thin high-� layer causes a substantial trapping of charge during hydrogen adsorption. As a result, the interface between the Pt electrode and the thin oxide layer becomes highly polarised. Measurement results also show that the nanoplatelets coated with Ta2O5 can enable the device to be more sensitive (a larger voltage shift under hydrogen exposure) than those coated with La2O3.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.